Advertisement

Differential Gene Expression Analysis of Plants

  • Mark ArickII
  • Chuan-Yu Hsu
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1783)

Abstract

Since the next-generation sequencing (NGS) systems were invented and introduced to life science research about a decade ago, the NGS technology has extensively utilized in wide range of genomic, transcriptomic, and evolutionary studies. Compared with other eukaryotic species, the application of NGS technology in plant research reveals some challenges in sample preparation and data analysis due to some structural and physiological characteristics and genome complexity nature in plants. Hence, despite of the standard sample preparation and data process protocols widely used in high throughput transcriptomic analysis, we also describe the modified hot borate RNA extraction protocol specific for high quality and quantity plant total RNA isolation, and some comments and suggestions to achieve better assessments in the validation of RNA and library quality and data analysis.

Key words

RNA-Seq Hot borate Transcriptome Differential gene expression analysis Illumina 

References

  1. 1.
    Wan CY, Wilkins TA (1994) A modified hot borate method significantly enhances the yield of high-quality RNA from cotton (Gossypium hirsutum L.) Anal Biochem 223:7–12. https://doi.org/10.1006/abio.1994.1538 CrossRefPubMedGoogle Scholar
  2. 2.
    Schroeder A, Mueller O, Stocker S, Salowsky R, Leiber M, Gassmann M, Lightfoot S, Menzel W, Granzow M, Ragg T (2006) The RIN: an RNA integrity number for assigning integrity values to RNA measurements. BMC Mol Biol 7:3–16. https://doi.org/10.1186/1471-2199-7-3 CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Die JV, Román B (2012) RNA quality assessment: a view from plant qPCR studies. J Exp Bot 63:6069–6077. https://doi.org/10.1093/jxb/ers276 CrossRefPubMedGoogle Scholar
  4. 4.
    Johnson MT, Carpenter EJ, Tian Z, Bruskiewich R, Burris JN, Carrigan CT, Chase MW, Clarke ND, Covshoff S, Depamphilis CW, Edger PP, Goh F, Graham S, Greiner S, Hibberd JM, Jordon-Thaden I, Kutchan TM, Leebens-Mack J, Melkonian M, Miles N, Myburg H, Patterson J, Pires JC, Ralph P, Rolf M, Sage RF, Soltis D, Soltis P, Stevenson D, Stewart CN Jr, Surek B, Thomsen CJ, Villarreal JC, Wu X, Zhang Y, Deyholos MK, Wong GK (2012) Evaluating methods for isolating total RNA and predicting the success of sequencing phylogenetically diverse plant transcriptomes. PLoS One 7:e50226. https://doi.org/10.1371/journal.pone.0050226 CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Sambrook J, Fritsch EF, Maniatis T (1989) Molecular cloning: a laboratory manual. Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NYGoogle Scholar
  6. 6.
    Wang Y, Ghaffari N, Johnson CD, Braga-Neto UM, Wang H, Chen R, Zhou H (2011) Evaluation of the coverage and depth of transcriptome by RNA-Seq in chickens. BMC Bioinformatics 12:S5PubMedPubMedCentralGoogle Scholar
  7. 7.
    Williams CR, Baccarella A, Parrish JZ, Kim CC (2016) Trimming of sequence reads alters RNA-Seq gene expression estimates. BMC Bioinformatics 17:103CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Conesa A, Madrigal P, Tarazona S, Gomez-Cabrero D, Cervera A, McPherson A, Szcześniak MW, Gaffney DJ, Elo LL, Zhang X et al (2016) A survey of best practices for RNA-seq data analysis. Genome Biol 17:13CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Yoo M-J, Szadkowski E, Wendel JF (2013) Homoeolog expression bias and expression level dominance in allopolyploid cotton. Heredity 110:171–180CrossRefPubMedGoogle Scholar
  10. 10.
    Hornett EA, Wheat CW (2012) Quantitative RNA-Seq analysis in non-model species: assessing transcriptome assemblies as a scaffold and the utility of evolutionary divergent genomic reference species. BMC Genomics 13:361CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Srivastava A, Sarkar H, Gupta N, Patro R (2016) RapMap: a rapid, sensitive and accurate tool for mapping RNA-seq reads to transcriptomes. Bioinformatics 32:i192–i200CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Samans B, Yang Y, Krebs S, Sarode GV, Blum H, Reichenbach M, Wolf E, Steger K, Dansranjavin T, Schagdarsurengin U (2014) Uniformity of nucleosome preservation pattern in Mammalian sperm and its connection to repetitive DNA elements. Dev Cell 30:23–35CrossRefPubMedGoogle Scholar
  13. 13.
    Royo H, Stadler MB, Peters AHFM (2016) Alternative computational analysis shows no evidence for nucleosome enrichment at repetitive sequences in mammalian spermatozoa. Dev Cell 37:98–104CrossRefPubMedGoogle Scholar
  14. 14.
    Soneson C, Love MI, Robinson MD (2016) Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Research 4:1521CrossRefPubMedCentralGoogle Scholar
  15. 15.
    Bourgon R, Gentleman R, Huber W (2010) Independent filtering increases detection power for high-throughput experiments. Proc Natl Acad Sci 107:9546–9551CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institute for Genomics, Biocomputing & BiotechnologyMississippi State UniversityMississippi StateUSA
  2. 2.Institute for Genomics, Biocomputing & BiotechnologyMississippi State UniversityMississippi StateUSA

Personalised recommendations